University Teachers’ and Students’ Perceptions on Autonomous English Learning: A Case of a Private University in Vietnam
Bibliographic record
Abstract
Understanding teachers' perspectives on learner autonomy is essential for addressing issues related to autonomous learning, as these perspectives heavily influence instructional approaches and consequently impact the learning experiences of students. With regard to students' perceptions of their English language learning, they are not solely dependent on the teacher; instead, they take ownership of the decisions guiding their own learning process. To promote learner autonomy effectively, educators must simultaneously confront their own apprehensions about relinquishing some control over the classroom environment and improve their communication skills with students. The paper used mixed-method which combined between quantitative and qualitative methods to analyze how the teachers’ and students’ perspectives on autonomous English learning through their teaching and learning styles. The paper surveyed 1000 students and 40 English teachers, all of whom participated voluntarily in the survey. The study aims to assess the specific objectives (1) the level of learner autonomy among a private university (2) the perceptions of English teachers regarding learner autonomy (3) the perceptions of English-majored students regarding learner autonomy (4) the strategies and methods employed in promoting learner autonomy. The findings suggested that the students should enhance their self-directed learning and take more responsibility for their own learning to meet the program's learning outcomes and increase the teachers’ satisfaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".